AI is making spacecraft propulsion more efficient – and could even lead to nuclear-powered rockets
### The Future of Space Travel: How AI is Revolutionizing Rocket Propulsion
As humanity sets its sights on ambitious missions to the Moon, Mars, and beyond, the challenge of propulsion technology becomes increasingly critical. Each year, space agencies and private companies launch hundreds of rockets, a number expected to rise dramatically in the coming years. The success of these missions hinges on advancements in propulsion systems, which are essential for ensuring that spacecraft can travel faster, safer, and more efficiently through the cosmos. This is where artificial intelligence (AI) and, more specifically, machine learning (ML) come into play, offering groundbreaking solutions to age-old problems in rocket design and operation.
A team of engineers and graduate students is exploring how AI, particularly a subset known as reinforcement learning, can optimize spacecraft propulsion systems. Reinforcement learning enables machines to learn from experience, identifying patterns and improving performance over time. For instance, in the context of rocket propulsion, AI can analyze complex data to determine the most efficient trajectories for spacecraft or design better propulsion systems by selecting optimal materials and configurations. This capability is crucial for developing advanced propulsion methods like nuclear thermal propulsion, which utilizes the heat generated from nuclear reactions to propel spacecraft. Unlike conventional fuel-burning engines, nuclear propulsion could significantly reduce travel time to destinations like Mars, making it a promising avenue for future exploration.
Reinforcement learning is particularly beneficial during two key phases: the design of propulsion systems and their real-time operation during missions. In the design phase, AI can help engineers optimize the geometry and heat transfer processes between fuel and propellant, which are pivotal in maximizing thrust. Historical designs, such as those from NASA’s NERVA program, have evolved over the years, and AI is now enabling engineers to explore innovative configurations that were previously unimaginable. Moreover, in the operational phase, AI can assist in managing fuel consumption and adapting mission parameters in real-time, a necessity for spacecraft that must respond to dynamic conditions, such as military satellites that shift roles based on geopolitical needs.
The potential of AI in space propulsion is not just limited to nuclear thermal systems; it also extends to nuclear fusion technology, which promises even greater energy yields. Researchers are investigating compact designs like polywells that harness magnetic fields to create conditions suitable for fusion. Managing these magnetic fields is a complex task, and reinforcement learning can help optimize this process, paving the way for practical fusion propulsion systems. As we continue to push the boundaries of space exploration, the integration of AI into propulsion technology is set to redefine our capabilities, opening new frontiers in our quest to explore the universe.
Propulsion technology helps rockets get off the ground.
Joel Kowsky/NASA via AP
Every year, companies and space agencies
launch hundreds of rockets into space
– and that number is set to grow dramatically with ambitious missions to the Moon, Mars and beyond. But these dreams hinge on one critical challenge: propulsion – the methods used to push rockets and spacecraft forward.
To make interplanetary travel faster, safer and more efficient, scientists need breakthroughs in propulsion technology.
Artificial intelligence
is one type of technology that has begun to provide some of these necessary breakthroughs.
We’re a team of
engineers and graduate students
who are studying how AI in general, and a subset of AI called
machine learning
in particular, can transform spacecraft propulsion. From optimizing
nuclear thermal engines
to managing complex
plasma confinement in fusion systems
, AI is reshaping propulsion design and operations. It is quickly becoming an indispensable partner in humankind’s journey to the stars.
Machine learning and reinforcement learning
Machine learning is a branch of AI that identifies patterns in data that it has not explicitly been trained on. It is a vast field
with its own branches
, with a lot of applications. Each branch emulates intelligence in different ways: by recognizing patterns, parsing and generating language, or learning from experience. This last subset in particular, commonly known as
reinforcement learning
, teaches machines to perform their tasks by rating their performance, enabling them to continuously improve through experience.
As a simple example, imagine a chess player. The player does not calculate every move but rather recognizes patterns from playing a thousand matches. Reinforcement learning creates similar intuitive expertise in machines and systems, but at a computational speed and scale impossible for humans. It learns through experiences and iterations
by observing its environment
. These observations allows the machine to correctly interpret each outcome and deploy the best strategies for the system to reach its goal.
Reinforcement learning can improve human understanding of deeply complex systems – those that challenge the limits of human intuition. It can help determine the most
efficient trajectory for a spacecraft
heading anywhere in space, and it does so by optimizing the propulsion necessary to send the craft there. It can also potentially
design better propulsion systems
, from selecting the best materials to coming up with configurations that transfer heat between parts in the engine more efficiently.
In reinforcement learning, you can train an AI model to complete tasks that are too complex for humans to complete themselves.
Reinforcement learning for propulsion systems
In regard to space propulsion, reinforcement learning generally falls into two categories: those that assist during the design phase – when engineers define mission needs and system capabilities – and those that support
real-time operation
once the spacecraft is in flight.
Among the most exotic and promising propulsion concepts is nuclear propulsion, which harnesses the same forces that power atomic bombs and fuel the Sun:
nuclear fission and nuclear fusion
.
Fission works by splitting heavy atoms
such as uranium or plutonium to release energy – a principle used in most terrestrial nuclear reactors. Fusion, on the other hand,
merges lighter atoms
such as hydrogen to produce even more energy, though it requires far more extreme conditions to initiate.
Fission splits atoms, while fusion combines atoms.
Sarah Harman/U.S. Department of Energy
Fission is a more mature technology that has been tested in some space propulsion prototypes. It has even been used in space in the form of
radioisotope thermoelectric generators
, like those that
powered the Voyager probes
. But fusion remains a tantalizing frontier.
Nuclear thermal propulsion
could one day take spacecraft to Mars and beyond at a lower cost than that of simply burning fuel. It would get a craft there faster than
electric propulsion
, which uses a heated gas made of charged particles called plasma.
Unlike these systems, nuclear propulsion relies on heat generated from atomic reactions. That heat is transferred to a propellant, typically hydrogen, which expands and exits through a nozzle to produce thrust and shoot the craft forward.
So how can reinforcement learning help engineers develop and operate these powerful technologies? Let’s begin with design.
The nuclear heat source for the Mars Curiosity rover, part of a radioisotope thermoelectric generator, is encased in a graphite shell. The fuel glows red hot because of the radioactive decay of plutonium-238.
Idaho National Laboratory
,
CC BY
Reinforcement learning’s role in design
Early nuclear thermal propulsion designs from the 1960s, such as those in NASA’s
NERVA program
, used solid uranium fuel molded into prism-shaped blocks. Since then, engineers have explored alternative configurations – from beds of ceramic pebbles to
grooved rings with intricate channels
.
The first nuclear thermal rocket was built in 1967 and is seen in the background. In the foreground is the protective casing that would hold the reactor.
NASA/Wikipedia
Why has there been so much experimentation? Because the more efficiently a reactor can transfer heat from the fuel to the hydrogen, the more thrust it generates.
This area is where reinforcement learning has proved to be essential. Optimizing the geometry and heat flow between fuel and propellant is a complex problem, involving countless variables – from the material properties to the amount of hydrogen that flows across the reactor at any given moment. Reinforcement learning can analyze these design variations and identify configurations that maximize
heat transfer
. Imagine it as a smart thermostat but for a rocket engine – one you definitely don’t want to stand too close to, given the extreme temperatures involved.
Reinforcement learning and fusion technology
Reinforcement learning also plays a key role in developing nuclear fusion technology. Large-scale experiments such as the
JT-60SA tokamak
in Japan are pushing the boundaries of fusion energy, but their massive size makes them impractical for spaceflight. That’s why researchers are exploring
compact designs such as polywells
. These exotic devices look like hollow cubes, about a few inches across, and they confine plasma in magnetic fields to create the conditions necessary for fusion.
Controlling magnetic fields
within a polywell is no small feat. The magnetic fields must be strong enough to keep hydrogen atoms bouncing around until they fuse – a process that demands immense energy to start but can become self-sustaining once underway. Overcoming this challenge is necessary for scaling this technology for nuclear thermal propulsion.
Reinforcement learning and energy generation
However, reinforcement learning’s role doesn’t end with design. It can help manage fuel consumption – a critical task for missions that must adapt on the fly. In today’s space industry, there’s growing interest in spacecraft that can serve different roles depending on the mission’s needs and how they adapt to priority changes through time.
Military applications, for instance, must respond rapidly to shifting geopolitical scenarios. An example of a technology adapted to fast changes is
Lockheed Martin’s LM400
satellite, which has varied capabilities such as missile warning or remote sensing.
But this flexibility introduces uncertainty. How much fuel will a mission require? And when will it need it? Reinforcement learning can help with these calculations.
From bicycles to rockets, learning through experience – whether human or machine – is shaping the future of space exploration. As scientists push the boundaries of propulsion and intelligence, AI is playing a growing role in space travel. It may help scientists explore within and beyond our solar system and open the gates for new discoveries.
Sreejith Vidhyadharan Nair receives funding from the University of North Dakota. I have previously received external research funding from agencies such as the FAA and NASA; however, these projects were not related to nuclear propulsion systems.
Marcos Fernandez Tous, Preeti Nair, and Sai Susmitha Guddanti do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.
